Although factories and processing plants around the world are progressively adopting more automation to improve production, quality and efficiency, these efforts remain largely old-school, using hard-coded automation logic that endures statically once it is commissioned. Continuous marginal improvement is possible through the efforts of hands-on operations personnel, but once a system is in production, it is often difficult to initiate major changes.
But what if an automation platform could provide the intelligence and visualization needed to empower operations personnel to make certain adjustments in real time? These improvements could include minor changes, often suggested by data visualization and analytics, or significant changes driven by machine learning-enabled automated decision-making. This would allow organizations to move beyond basic automation and toward a much more advanced state of automation, keeping them competitive.
Modern industrial Internet of Things (IIoT) technologies, edge and cloud software, machine learning (ML), artificial intelligence (AI) and low-code platforms are enabling this type of advanced automation and putting companies on the path to benefitting from artificial intelligence of things (AIoT) implementations.
Taking An AloT Approach Led By Operational Know-How
Applying digital automation techniques to industrial operation challenges has been effective for decades, but this work is often gated behind capital projects requiring specialized engineering and programming skills. Once facilities are in production, human operators usually remain involved as partial or essential decision-makers, so why not explore ways to make greater use of these boots-on-the-ground experts?
Continue reading: https://www.forbes.com/sites/forbestechcouncil/2022/11/09/advancing-to-the-future-of-industrial-automation/?sh=2413834d3db8
But what if an automation platform could provide the intelligence and visualization needed to empower operations personnel to make certain adjustments in real time? These improvements could include minor changes, often suggested by data visualization and analytics, or significant changes driven by machine learning-enabled automated decision-making. This would allow organizations to move beyond basic automation and toward a much more advanced state of automation, keeping them competitive.
Modern industrial Internet of Things (IIoT) technologies, edge and cloud software, machine learning (ML), artificial intelligence (AI) and low-code platforms are enabling this type of advanced automation and putting companies on the path to benefitting from artificial intelligence of things (AIoT) implementations.
Taking An AloT Approach Led By Operational Know-How
Applying digital automation techniques to industrial operation challenges has been effective for decades, but this work is often gated behind capital projects requiring specialized engineering and programming skills. Once facilities are in production, human operators usually remain involved as partial or essential decision-makers, so why not explore ways to make greater use of these boots-on-the-ground experts?
Continue reading: https://www.forbes.com/sites/forbestechcouncil/2022/11/09/advancing-to-the-future-of-industrial-automation/?sh=2413834d3db8
